PROJECT TITLE :

Analyzing Interpersonal Empathy via Collective Impressions

ABSTRACT:

This paper presents a research framework for understanding the empathy that arises between individuals while they are conversing. By that specialize in the method by that empathy is perceived by other people, this paper aims to develop a computational model that automatically infers perceived empathy from participant behavior. To describe such perceived empathy objectively, we introduce the thought of using the collective impressions of external observers. In explicit, we have a tendency to focus on the actual fact that the perception of alternative’s empathy varies from individual to individual, and take the standpoint that this individual distinction itself is an important attribute of human communication for building, for instance, successful human relationships and consensus. This paper describes a probabilistic model of the process that we tend to built primarily based on the Bayesian network, which relates the empathy perceived by observers to how the gaze and facial expressions of participants co-occur between a combine. During this model, the chance distribution represents the diversity of observers’ impression, that reflects the individual differences in the schema when perceiving others’ empathy from their behaviors, and the paradox of the behaviors. Comprehensive experiments demonstrate that the inferred distributions are like those created by observers.